diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 4a4700232c..5d6b66ccf0 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -5,7 +5,7 @@ from typing import List, Optional, Union, Callable
 
 import PIL.Image
 import torch
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
@@ -260,8 +260,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if isinstance(init_image, PIL.Image.Image):
             init_image = preprocess(init_image.convert('RGB'))
 
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self._diffusers08_get_timesteps(num_inference_steps, strength)
+        img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
@@ -292,17 +293,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         return self.scheduler.add_noise(init_latents, noise, timestep)
 
-    def _diffusers08_get_timesteps(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps
-
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
             screened_images, has_nsfw_concept = self.run_safety_checker(